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ANN Model Design

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LitapAI edited this page May 25, 2026 · 1 revision

🧠 ANN Model Design

Overview

The PRISM Framework uses an Artificial Neural Network (ANN) to evaluate product relevance, market viability, and prioritization potential using structured metadata and engineered scoring features.

The ANN acts as the intelligence core of the PRISM system by learning hidden relationships between product attributes and strategic market success indicators.

Objective of ANN

The ANN is designed to:

  • Predict product relevance
  • Identify high-potential opportunities
  • Assist prioritization decisions
  • Reduce manual evaluation complexity
  • Improve strategic product analysis

Neural Network Architecture

Input Layer

The input layer receives structured product-related features such as:

  • Market demand score
  • Customer relevance
  • Innovation score
  • Competitive intensity
  • Pricing indicators
  • Product category encoding
  • Trend signals
  • Metadata features

Example:

Input Features = 12

Hidden Layers

The model contains multiple hidden layers responsible for pattern learning and feature abstraction.

Hidden Layer 1

Dense(64, activation='relu')

Purpose:

  • Initial feature extraction
  • Pattern recognition
  • Non-linear relationship learning

Dropout Layer

Dropout(0.3)

Purpose:

  • Prevent overfitting
  • Improve generalization
  • Stabilize learning

Hidden Layer 2

Dense(32, activation='relu')

Purpose:

  • Deep feature refinement
  • Strategic signal compression
  • Relevance optimization

Output Layer

Dense(1, activation='sigmoid')

Purpose:

  • Generate final product relevance probability score
  • Binary or probabilistic classification

Output Example:

Score Interpretation
0.90 Highly Relevant
0.65 Moderately Relevant
0.30 Low Relevance

Activation Functions

ReLU (Rectified Linear Unit)

Used in hidden layers.

Advantages:

  • Fast computation
  • Efficient gradient propagation
  • Better deep learning performance

Sigmoid Function

Used in output layer.

Purpose:

  • Converts output into probability between 0 and 1
  • Useful for classification scoring

Training Configuration

Parameter Value
Optimizer Adam
Loss Function Binary Crossentropy
Epochs 50
Batch Size 32
Validation Split 20%

Model Training Workflow

Dataset
   ↓
Feature Engineering
   ↓
Normalization
   ↓
Train/Test Split
   ↓
ANN Training
   ↓
Prediction
   ↓
Scoring & Ranking

Evaluation Metrics

The ANN model performance is evaluated using:

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • Loss Curves
  • Validation Accuracy

Why ANN Was Chosen

ANN was selected because:

  • Product-market relationships are non-linear
  • Multiple hidden correlations exist
  • Traditional scoring systems are limited
  • Neural networks improve adaptive intelligence

Future Enhancements

Future ANN improvements may include:

  • Deep Neural Networks (DNN)
  • Attention mechanisms
  • Transformer-based scoring
  • Reinforcement learning agents
  • Real-time adaptive retraining
  • Multi-modal product intelligence

Summary

The ANN engine serves as the computational intelligence layer of the PRISM Framework, enabling scalable, data-driven product prioritization and strategic evaluation using machine learning methodologies.

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